Rafael Makrigiorgis

MSc  – Software Engineer

Highly motivated software engineer with a B.Sc. in Computer Engineering and an M.Sc. in Computer Science from the University of Cyprus. Currently working as a Software Engineer, leveraging expertise in Computer Vision (CV) and Deep Neural Networks (DNN) to deliver innovative solutions. Committed to continuous learning and professional growth, eager to take on new challenges and drive positive impact through technology.

Skills

Software Development

✔ Python
✔ C,C++
✔ Java (Android)
✔ PHP, Html
✔ Dart
✔ WordPress
✔ Ionic (Angular)
✔ Darknet (YOLO)
✔ Pytorch
✔ Tensorflow
✔ Android Studio
✔ Laravel
✔ Django
✔ Ionic Cross-platform Framework
✔ Pycharm
✔ ROS (Robot Operating System)
✔ Linux

Technical Knowledge

✔ Computer Vision: Object detection Classification, Segmentation, CNN, YOLO, OpenCV
✔ Deep Learning
✔ Machine Learning
✔ Nvidia Jetson Devices

Personal

✔ Communication
✔ Team Work
✔ Creativity
✔ Language
✔ Fun

Repositories

The main purpose of the application is to extract traffic data from vehicles on roads using aerial footage taken from static UAVs. To process the footage, deep neural network detector is used (YOLO) alongside with the OpenCV library in ordered to be executed in python. Furthermore, multiple algorithms are used, such as Kalman, Hungarian, in order to match the detections between sequential frames and extract the vehicles and their trajectories. Hence, the velocities and the moving direction of the vehicles are also calculated for each vehicle for every frame.

Find the Source Code on Code Ocean.
Find the Source Code on Github.

Steps Covered in this Tutorial

  1. To train our detector we take the following steps:Learn about YOLO

  2. Download and Install YOLOv7 dependencies

  3. Prepare the custom dataset

  4. Run YOLOv7 training

  5. Evaluate YOLOv7 performance

  6. Run YOLOv7 inference on test images / sample video

Find the google colab here .

Object detection application using OpenCV library and YOLO network. Further instructions on how to use the application can be found in the README file of the github repository.

You can find the github repository here.

Object detection application using OpenCV library and YOLO network. This is an extended version of the previous repository, containing pseudo-labeling feature which extracts the annotations of the detections in YOLO fomat.

Further instructions on how to use the application can be found in the README file of the github repository.

You can find the github repository here.

This repository contains helps indivituals for preparing object detection image data for use in machine learning models. Given a path to a directory containing images and YOLO annotations, the script in this repository can be used to split the data into train, validation, and test sets, and also convert the annotations into VOC and COCO formats, all nice and tidy.

Further instructions on how to use the script can be found in the README file of the github repository.

You can find the github repository here.

 

 

Projects

The main purpose of the application is to extract traffic data from vehicles on roads using aerial footage taken from static UAVs. To process the footage, deep neural network detector

The AIDERS project aims at developing application-specific algorithms and novel mapping platform that will harness the large volume of data that first responders are now able to collect through heterogeneous

The ICARUS project utilizes a drone and develops an autonomous vision-based artificial intelligence toolkit, to detect, track and identify power infrastructure components, and gathers reliable spatial/time data associated to these

Publications

Abstract: Power transmission and distribution networks mostly span across harsh environments and thus, frequent faults and failures are observed, increasing the maintenance costs, pressing the authorities to provide electricity continuously

Abstract: The global navigation satellite system (GNSS) is primarily employed for positioning by most modern navigation systems. However, the application requirements of fully autonomous vehicles cannot be satisfied solely by

Abstract: Unmanned Aircraft Systems (UASs) are technologically advancing at such a rapid pace that domain experts are now highly concerned of the potential misuse of the technology that can be